
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "57"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 100 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 96 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.067812	10.275408	78.659205	82.482312	20.981833	26.541908	26.287670	30.030024	0.0502	0.0410	0.0031	1.226681	1.207176
2459593	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.917239	17.100314	82.836576	87.375328	30.401877	37.338314	13.600228	12.250280	0.0524	0.0387	0.0062	1.437709	1.374956
2459592	RF_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.654088	1.643039	7.670062	8.391123	0.167671	0.978385	-1.654613	0.450729	0.0411	0.0284	0.0010	nan	nan
2459591	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.510254	19.791456	96.778049	101.383066	26.409854	32.256411	1.326546	2.462274	0.0513	0.0356	0.0076	1.569612	1.456616
2459590	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.668471	24.005684	89.803586	94.389875	27.882812	34.307006	3.109121	4.386345	0.0494	0.0368	0.0056	1.484722	1.426506
2459589	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.709303	22.606854	83.443905	87.419662	16.568595	21.571514	18.640222	17.309146	0.0516	0.0427	0.0031	1.219532	1.205420
2459588	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.327391	15.534715	101.541045	106.963701	21.636773	24.586109	14.485347	13.311288	0.0498	0.0392	0.0030	1.544235	1.423747
2459587	RF_ok	100.00%	-	-	-	100.00%	0.00%	18.802901	21.122946	129.744487	136.889747	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	1.232932	1.234332
2459586	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.686030	18.908318	31.810637	32.875658	9.798115	11.460455	6.635091	7.963011	0.0546	0.0406	0.0074	1.582609	1.466055
2459585	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.955134	25.975732	10.394805	11.396808	9.312595	10.068549	1.481376	4.298792	0.0407	0.0288	0.0014	0.000000	0.000000
2459584	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.214600	25.029389	111.688207	117.194687	16.210746	18.445823	24.140904	23.929774	0.0455	0.0346	0.0048	0.000000	0.000000
2459583	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.589596	19.095702	34.592749	36.267043	19.574970	23.235310	-0.055519	0.414935	0.0513	0.0377	0.0062	2.164324	1.872599
2459582	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.579796	16.249514	31.092849	32.904561	23.484577	27.997525	0.009150	0.530699	0.0483	0.0352	0.0063	1.758395	1.572274
2459581	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.818116	22.768253	36.001833	37.127836	18.454680	22.504616	0.235488	0.595596	0.0504	0.0386	0.0050	1.487593	1.433648
2459580	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.395846	17.114549	36.165043	38.021022	20.477677	23.647588	-0.241923	0.378887	0.0518	0.0414	0.0041	2.082533	1.787782
2459579	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459578	RF_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.204121	1.672163	2.986349	3.142234	-0.226250	-0.012483	0.548592	1.904134	0.0328	0.0289	0.0005	nan	nan
2459577	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.877868	21.787393	33.426687	34.700760	20.569597	24.524744	0.096378	0.635003	0.0482	0.0356	0.0056	1.222973	1.214828
2459576	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.357163	16.826037	26.556413	27.812041	18.793907	21.620783	0.741167	1.289444	0.0505	0.0368	0.0066	1.916390	1.524829
2459575	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.543596	17.344254	26.356998	27.682198	15.460131	16.992743	7.293821	6.399671	0.0502	0.0392	0.0047	0.944914	0.932112
2459574	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.734512	15.841974	25.280654	26.455934	20.402748	24.154743	1.094452	0.794422	0.0487	0.0365	0.0051	1.759185	1.452300
2459573	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.338068	16.316734	43.559922	45.530380	24.322891	27.880959	0.643545	1.663281	0.0502	0.0390	0.0054	0.000000	0.000000
2459572	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.166375	16.273578	25.390617	26.596927	15.621064	17.712937	1.783314	2.914919	0.0522	0.0387	0.0067	2.427454	1.776796
2459571	RF_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.702143	1.308843	2.868845	3.221551	-0.247995	-0.263861	0.442662	2.172088	0.0315	0.0289	0.0010	nan	nan
2459570	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.429968	21.067756	15.298801	16.512369	1.252821	1.750595	2.604753	3.475546	0.0578	0.0471	0.0047	1.276374	1.286193
2459569	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.599363	19.593564	33.400379	34.549716	10.619141	10.832991	8.335874	9.132280	0.0553	0.0411	0.0058	1.662191	1.410105
2459566	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.049130	15.891664	33.184999	34.526441	18.733500	21.506711	-0.413692	-0.042919	0.0446	0.0342	0.0048	1.180928	1.150589
2459565	RF_ok	100.00%	-	-	-	100.00%	0.00%	12150.971607	11576.117065	89064.159110	78052.728156	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.390829	14.393578	35.177198	36.668618	2.151488	2.223332	0.186192	0.781428	0.0620	0.0485	0.0091	2.008505	1.437062
2459563	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.794685	10.494993	17.382645	18.126467	2.338757	2.657546	0.621192	1.131927	0.0621	0.0498	0.0084	2.085381	1.470230
2459561	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.444023	11.107932	16.589106	17.291715	2.704392	2.893359	1.035265	1.860893	0.0663	0.0525	0.0090	2.618844	1.616226
2459560	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.597432	10.085559	17.025604	17.529007	2.415412	2.919227	0.654509	1.091694	0.0579	0.0468	0.0067	2.483827	1.577388
2459559	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.076738	12.987744	18.568672	19.212309	2.731798	3.233878	0.879632	1.521260	0.0645	0.0533	0.0078	1.758254	1.371890
2459558	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.724872	17.228688	34.922432	36.819699	21.203878	23.032789	1.233392	2.226867	0.0591	0.0479	0.0069	1.192317	1.181422
2459557	RF_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0385	0.0369	0.0009	nan	nan
2459556	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.104731	17.450786	37.694182	39.824251	19.211613	20.810596	2.651480	4.978345	0.0637	0.0522	0.0076	1.208704	1.193435
2459554	RF_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0554	0.0440	0.0071	nan	nan
2459553	RF_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0637	0.0540	0.0069	nan	nan
2459552	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.663903	21.380848	50.768079	52.492351	29.360314	32.256556	3.043654	3.811056	0.0577	0.0496	0.0057	1.067651	1.222111
2459551	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.537388	10.640109	20.537516	21.286950	3.252944	4.122445	1.012095	1.721016	0.0610	0.0499	0.0078	1.591850	1.328913
2459550	RF_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0340	0.0307	0.0018	nan	nan
2459549	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.436214	16.259748	38.182743	39.665707	20.692364	22.725371	1.029331	2.067989	0.0562	0.0492	0.0046	1.274839	1.269515
2459542	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.376508	23.221409	77.126602	80.018388	1.140168	2.018858	0.552074	1.333804	0.0595	0.0509	0.0058	1.265149	1.250777
2459541	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.512634	9.954877	38.258129	37.378270	223.300418	264.896651	7.724733	16.076136	0.0686	0.0595	0.0058	1.147326	1.135548
2459540	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.626257	16.733287	17.318320	18.311285	4.134229	5.334154	0.636094	1.186732	0.0599	0.0521	0.0044	1.248554	1.241905
2459536	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.627805	28.297407	65.844889	68.055851	29.293469	31.582499	2.223874	3.658069	0.0517	0.0463	0.0032	1.410896	1.397888
2459535	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.559237	13.871546	16.092048	16.808178	2.435463	5.746282	0.135085	0.838499	0.0410	0.0381	0.0013	1.168778	1.163704
2459534	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.823714	13.637046	14.341793	14.921650	2.433060	4.169903	-0.063797	0.193432	0.0442	0.0411	0.0013	1.154921	1.149489
2459533	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.170526	20.979294	35.650894	37.291295	17.793423	20.670704	1.054825	2.444943	0.0431	0.0394	0.0015	1.132897	1.126832
2459532	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.443753	23.165653	45.892042	48.338652	22.075489	25.495018	0.405261	1.268698	0.0435	0.0408	0.0012	0.000000	0.000000
2459530	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.155663	11.838128	14.513650	15.099012	3.340984	4.212371	0.342869	0.688751	0.0466	0.0439	0.0014	1.189534	1.183978
2459527	RF_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.373891	28.866235	53.926414	56.206194	40.484748	35.060977	-0.008663	1.029452	0.0689	0.0837	0.0090	0.000000	0.000000
2459523	RF_ok	100.00%	-	-	-	100.00%	0.00%	21.954792	24.527819	52.336019	53.692071	17.744919	29.784691	1.902202	3.021156	nan	nan	nan	1.356789	1.331023
2459522	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.726776	23.874198	49.333435	52.214809	10.917966	23.761762	0.887307	2.273185	0.0607	0.0534	0.0031	1.413245	1.390406
2459513	RF_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.107891	1.671422	3.068734	3.559174	-0.158310	0.433713	0.649777	2.996142	0.0519	0.0443	0.0098	nan	nan
2459508	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.829658	32.643583	50.429515	52.950826	18.174805	15.857660	12.750140	14.245700	0.0431	0.0421	0.0004	0.000000	0.000000
2459505	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	83.195176	114.092461	112.974912	114.665592	97.444761	103.895660	610.701365	586.048119	0.0098	0.0069	0.0054	1.100052	1.099520

Antenna Season Report Notebook¶

Antenna 57 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 57, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶